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Many neural text-to-speech architectures can synthesize nearly natural speechfrom text inputs. These architectures must be trained with tens of hours ofannotated and high-quality speech data. Compiling such large databases forevery new voice requires a lot of time and effort. In this paper, we describe amethod to extend the popular Tacotron-2 architecture and its training with dataaugmentation to enable single-speaker synthesis using a limited amount ofspecific training data. In contrast to elaborate augmentation methods proposedin the literature, we use simple stationary noises for data augmentation. Ourextension is easy to implement and adds almost no computational overhead duringtraining and inference. Using only two hours of training data, our approach wasrated by human listeners to be on par with the baseline Tacotron-2 trained with23.5 hours of LJSpeech data. In addition, we tested our model with asemantically unpredictable sentences test, which showed that both modelsexhibit similar intelligibility levels.

Low-Resource Text-to-Speech Using Specific Data and Noise Augmentation